How to Use LLMs Effectively for Coding

llm.MD

🧠 How to Use LLMs Effectively for Coding (2025 Edition)

LLM-assisted coding has evolved dramatically. If you're coming from the Copilot autocomplete era, the modern agentic workflow will feel like a superpower. This guide outlines practical patterns that work in real-world software engineering.

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1. Create Project Context Before Doing Anything Else

Agentic LLMs only work well when they understand your project.

Think of this like onboarding a brand-new junior dev:

• What would you explain on their first day?
• What would they need to know before touching code?

Minimum viable context:

• Your project README
• Folder structure (frontend, backend, infra, tests)
• Stack overview (frameworks, libraries, languages)
• Coding conventions
• Required workflows (tests, CI rules, branching strategy)
• Architectural preferences (e.g., “we use service objects over fat controllers”)

You can:

• Paste this into a Cursor project rule
• Use a prebuilt Cursor template
• Or ask your LLM to generate a contextual README based on your repo

Why it matters:
Without context, the model guesses — leading to hallucinations, incorrect assumptions, and giant unwanted changes.

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2. Use a Requirements Doc + Task List for All Work

This is the biggest productivity multiplier.

The flow used at Amazon and other engineering orgs:

1. LLM asks you clarifying questions first
   Prevents massive wrong assumptions.

2. Generate a requirements document
   Inputs, outputs, constraints, edge cases, success criteria.

3. Convert that into a hierarchical task list
   Parent tasks → subtasks with controllable granularity.

4. Execute tasks based strictly on that plan
   This eliminates scope drift and conversational chaos.

A lightweight open-source version of this workflow:
https://github.com/snarktank/ai-dev-tasks

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3. Start With Cursor

People try many tools, but the actual pattern is:

Experiment → get frustrated → return to Cursor for real work.

Cursor advantages:

• Strong agentic capabilities
• Excellent guardrails
• MCP integration (tools, APIs, Playwright, UI agents, DB calls)
• Works with any model (GPT, Claude, etc.)
• Deep integrations across your workflow:
  • You can start a background agent from your IDE
  • See its changes as branches/PRs on GitHub
  • Chat with the same agent in:
    • the IDE
    • the browser
    • GitHub (e.g. on PRs)
• IDE-native experience

Other tools have their place:

• Claude Code for deep analysis or refactors
• Devins / Cline for single-file execution
• Continue for offline or open-source models

But for getting started with modern LLM-based software development, Cursor is the most reliable and ergonomic.